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Quintus V. (talk | contribs) revert reinsertion of highly specific research work (citespam) |
Mention crowdsourcing and human-in-the-loop learning. |
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There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning,<ref name="multi"/> hybrid active learning<ref name="hybrid"/> and active learning in a single-pass (on-line) context,<ref name="single-pass"/> combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, [[incremental learning]] policies in the field of [[online machine learning]].
Large-scale active learning projects may benefit from [[crowdsourcing]] frameworks such as [[Amazon Mechanical Turk]] that include many [[human-in-the-loop|humans in the active learning loop]].
==Definitions==
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